It is amazing to see how
advances in AI make public think that AI is already almost the same as I
(Intelligence).

Yes, the progress in AI is
huge!

But so far nothing achieved
in this field shows actual intelligence, a.k.a. intellect.

An old but appropriate joke
comes to mind.

Researches study the
development of intellectual abilities in monkeys. They designed a cage with a
tree and hanged a banana on a branch to keep the banana high enough. Monkey
enters the cage and sees the banana. He tries to jump but the banana is too
high. He tries to clime the tree, but it is covered with plastic so the monkey
just slides back. Monkey looks around and sees a stick. He takes the stick and
hit the banana down. Success! Researchers are preparing the cage for the new
run; they place a new banana on the tree. Suddenly a hungry physics graduate
gets in the cage. He sees banana. He jumps, but the banana is too high. He
tries to clime the tree but slides back. He starts to shake the tree, but the
banana holds tightly to the branch. He keeps shaking the tree. Nothing happens.
Finally researches tell the guy via an intercom: “Hey, take a second, think it
through.” “What to think about”, the guy says, “it is simple - just shake and
shake hard”.

This joke helps to make a
clear difference between an intelligent action and the one that is not.

Almost all authors talk about
self-driving cars, or a robot that can walk and “talk”, or the three famous
AIs, Deep Blue, Watson, and AlphaGo because they won in chess, Jeopardy and Go.
Keep in mind, that all these AIs have been trained on using a vast set of
previously uploaded information. The work they do is not really cognitive, it
is routine. Of course, it is a highly performed routine work, but still –
routine. It is impressive, but it could never have been done without advances
in the amount of trivial operations a computer can make every second. When
trying to separate a “routine” work from a “non-routine” work (meaning
“intelligent”) people say that a routine work is the one done by body, and a
cognitive work is the one which mostly requires our brain – but that is just
plain wrong. Any work, or any action in general, requires functioning brain
(that is true for all animals, including humans). The difference is what kind
of problems does the brain solve when functioning.

There is a lot of routine
work a brain does every day, or even every minute. However, nowadays we need to
reassess the meaning of the term “routine”; it is not just something
repetitive, it is also something that can be done by “routinely” checking a
vast amount of data against a set of given criteria. No doubt a computer can do
this kind of a routine work much faster than a human, but that does not make it
more intelligent than a human – just faster.

Unfortunately, following AI
promoters, public also get a deviated impression of what true intelligence is.
Many think that memorizing lots of facts and quickly answering any question
from an encyclopedia is the sign of intelligence.

The purpose, the missions,
the central ability, the most important feature, and the signature sign of an
intellect (a.k.a. intelligence) is an ability to solve problems. If we want to
broad this definition of an intellect, we should say that the central ability
of an intellect is an ability to self-teach, to self-propel individual
knowledge generation, usually via generating a solution to a problem which has
not been solved before (hence, the solution has not been stored in the memory
and could not be found be a simple search, even via a very deep search, and the
action solving a problem at hand could not be previously trained). If we have
to select a single action which indicates the level of intelligence it will be
making a decision. Simple decisions do not require much of thinking. Although
today the term “simple” incudes decisions which can be made based on a fast
analysis of a large amount of data, as long as the parameters and values are
well established and well prepared. Complicated decisions are synonymous to not
trivial decisions, which often come spontaneously, as an insight. Spontaneity
means that an action has not been trained before and it requires taking a step
outside of the previously trained actions, and it has to happen here and now. No
robot can do this so far.

Yes, a robot can recognize
our face and voice, can get up if pushed down, can perform commands – but so
can a monkey, or a dog. Some dogs might show a very smart behavior, but an
intelligent dog does not exist.

A brain is just a biological
foundation for intelligence, but learning from an intelligent teacher is the
source and the roots of intelligence. You can try to train a monkey, but it
will not learn how to solve physics problems. On the other hand, you can find a
human child razed by monkeys and his perfectly human brain will not be able to
show any sign of a high intelligence.

So, how will we know when AI
is truly I (Intelligence)?

Another joke (a.k.a. an
anecdote!) comes to mind.

A young math teacher enters
the teacher lounge and swears: “My students are so stupid. I explained them a
theorem, they did not get it. I explained another time. Still did not get. I
explained the third time. I GOT IT! They still didn't”

(FYI: who is acting stupid in
this joke?).

There is a reason people say
“if you really want to understand something, teach it to someone”. Teaching is
a sign of a high intelligence. Of course, it needs to be a true teaching, but
not just routine training of repetitive actions. Animals also teach their cubs
to live, to hunt. Many of the current “teaching” software” do not go farther
than an animal-type intelligence; they train a student in a way a trainer
trains an animal do tricks, but no more.

Let’s stress one more time: the
key ability of an intellect is not an ability to walk, it is not an ability to
talk, it is an ability to solve problems. And so far, the most powerful
problem-solving instrument invented by the nature is a human brain. So, what
every AI expert should do is to watch what problems, in what order and how a human
brain learns to solve.

That means a simple thing, AI
experts need to map a problem-solving evolution of a growing human, from a baby
(problems to solve: reach, touch, recognize, walk, etc.), to … well, Albert Einstein!

At first a robot should learn
how to solve any problem from a standard physics textbook; then a robot should
be able to tutor a single student how to solve physics problems, and then, when
a robot can replace a high school physics teacher, I will agree that finally AI
has become I (Intelligence); but so far no company in the world (NO COMPANY IN THE WORLD) works on this (not
"sexy", I guess, or too difficult, or both).

Tuesday, March 14, 2017

This song has been written during a snow storm.When one is being "locked" in a house, mind starts wondering, and here we are!Keep in mind, I am an amateur song writer, and I learned English using books, TV and radio shows.I was just having fun, but anyone is welcome to use this text as a draft for an actual professional song!To deal with my responsibilitiesI need to have the right abilities,I have to learn, and do it fast,If I don’t want to be the last:- The last to wear commencement robe,- The last to find a better job, - The last promoted at the work,- The last erasing boss’s smirk.Education is important, Education is a must,Education is a port at Path to future from the past.Take your time and be persistent,Make your teacher sweat and grow,Education needs commitmentSame like making into PRO.(regarding the meaning of PRO - see the picture!)

The same approach must be used to eradicate all “the ignorance” in the world by reforming the way education currently is being reformed.

This task however is even more difficult than “eradicating all diseases” (http://www.teachology.xyz/30uS.html). Like in medical and biological research, research in education is being conducted by many independent groups, with a very low level of sharing data – mainly, because there is no comparable data (http://www.teachology.xyz/FW.htm). Many of the activities are not even a research, but an attempt to advance some elements of social reality in the field of education.

When the widow of late Steve Jobs, Ms. Laurene Powell Jobs announced her XQsuperschool initiative, I wrote her a letter, warning that there is a mismatch between the goal (reshaping ALL high schools in America) and the actions (reshaping 5 high school): http://www.teachology.xyz/xq.htm. There are 10 XQsuperschools now, but my premises in the letter still stand.

I got a hope again when Mr. Charles Chen Yidan announced the establishment of the Yidan Prize Foundation (http://www.yidanprize.org/en/). This is the first philanthropist who seems understands the difference between a social project and a scientific research. The distinction is very important for advancing education (http://www.teachology.xyz/wwNSF.html), and I applaud Mr. Charles Chen Yidan.

Of course, teachers and schools keep doing the best they can to give students the best education they can. They would appreciate any additional funds which would let them teach better. But simply giving extra money would not lead to a development of a science of education, would not advance a progress in new teaching technologies.

The latest reports show that U.S. system does not help many students to be ready for getting college education, especially in science and engineering.

“The number of U.S. citizens and permanent residents earning graduate degrees in science and engineering fell 5 percent in 2014 from its peak in 2008. At the same time, the number of students on temporary visas earning the same degrees soared by 35 percent”.

“Nearly half of PhD aerospace engineers, over 65% of PhD computer scientists, and nearly 80% of PhD industrial and manufacturing engineers were born abroad”

At this stage, any “innovations” at a college level are more like a game. The focus must be at the advancing pre-college education on a broad scale. However, at a K1 – K12 levels all “innovations” fall into two categories: (a) give teachers more workshops; (b) give students more toys (like tablets, Lego robots, etc.) – they do not represent a scientific research.

I have spent some time to study the materials related to the Yidan Prize.

I truly admire the mission of the Foundation, which is to create a better world through education.

I have watched the videos, I read all the information about the Yidan Prize.

The video and the Forecast point at several important problems the world is facing right now, for example how many children are not having any formal education, or that education does not guarantee a job, or on youth unemployment, and STEM graduates.

The Forecast shows the tendency of the future.

But education also has a long history.

We can imagine a long line which represents the trajectory of the evolution of world education. The Forecast indicates how this line will continue in the future.

I assume, that when the Yidan Prize was established, the goal was to alternate the current trajectory, to “bend the line”, so to speak. The actual trajectory of the evolution of the world education should become different from the projected trajectory (without the establishing of the Yidan Prize) due to the fact of the influence of the Yidan Prize.

But the Yidan Prize Foundation is not the only organization with a similar mission.

For example, the U.S. Department of Education appropriates about 69 billion dollars per a year. About 500 million dollars from the budget are spent to support innovations in education.

In addition to it, the National Science Foundation spends about 61 million dollars on research in the field of education.

1) “We want our students to do better. For that we plan on trying - this.” – this project mostly involves faculty or teachers who directly teach students.

or

2) “We want our school teachers to teach better. For that we plan on trying - this.” – this project mostly involves faculty from a university or a school of education helping teachers to teach better (usually via workshops, or other forms of communication).

The second and the third areas represent the areas of a scientific research.

For the second area, the main idea is that data must be collected from a vast number of sources (at least hundreds) – only then it will become the Big Data.

For the third area, I would use an analogy.

Among many new things America brought to the world is potato. There are more than 4000 types of potato. For each type, we know exactly how to grow it: what type of soil is good, when to plant, how often to water, what microelements to add, when the first leaf should start growing, what signs of a good or a bad growing process, etc.

But when we teach, we only know in general how people learn. But we have no idea about specific stages needed to learn a specific skill of a specific subject depending on the economical, racial, geographical, background of a student, his or her age, gender. And so far, no one does this type of a research.

When my students tell me that they want to make a difference in the world, I tell them:

“You want to make a difference? Be different!”

But it is simple - to paint your hair in pink. The true difference comes from thinking and acting differently, and from finding people who think and act differently and supporting those people.

What I see is that theYidan Prize is expected to be different from others by making a clear distinction between scientific projects in education (Education Research), and social projects in education (Education Development).

I only want to warn you that sometimes it is not easy to recognize the type of a project based only on its textual description.

I wish you good luck!

Dr. Valentin Voroshilov

Part III: Topics for further discussion

Education is the most important human practice. If I had to think about how to change education as a whole practice, at first I would ask myself, what is the missions of education in general? Then I would apply this view to the actual practice of education and compared.

The Yidan Prize is “to embrace outstanding achievements in education research and development”; but those achievements might belong to different social scales – individual, institutional, regional. It is advisable to keep in mind that in social practices (like education) an outstanding achievement on an individual level might have no effect on other levels.

The systemic approach to funding education should include this question: “How to manage funds more efficiently”. The society does not really want to know how students get good education. The society just wants to have students with good education. That is why in principle, it does not matter where and how students have been taught. But we really have to establish a uniform procedure for assessing the quality of education. That will mean that we will be able separate the process of learning from the process of assessing the results of learning. The quality control should be decoupled from a teaching process. This approach will eventually lead to more effective distribution of funds in education.

Every large research university has a long line of students who want to get education in those universities. That is why any internal research in such a university does not really make a broad impact, even if the university has structures which create many teaching tools. But an external outreach to schools, school district might make a big difference.

Why do people select a massive open online course? Because they do not have another option (due to financial, time, geographical restrictions). Currently, there is no MOOC which would be as good as a good regular face-to-face course. Creating such a MOOC would be a true breakthrough (but even bigger achievement would be creating a system of MOOCs: http://www.teachology.xyz/chs.htm).

The challenges education faces today have been facing education for decades. Education has “survived” many waves of innovations, so to speak. Big corporations and small startups develop a vast amount of various teaching tools. Teachers are flooded by innovative tools. It is like you buy a car, but instead of a car you get a kit, a collection of parts, and you need to assemble it, like a chair from IKEA.

Creativity, communicative skills are important. But if people cannot read or count, creativity will be useless. The current discussion is framed as “creativity versus basic skills”. Instead we need to be able to teach basic skills and develop creativity. Teaching creativity is not about what to teach, but how: it is not about the content, but about the process.

Good teaching leads to good results. Period. This statement is a law (http://www.teachology.xyz/6LT.html). If there are no good results, the teaching was not good. Simple. The quality of teaching is based on the quality of teacher professional development; the low quality of teaching is the direct result of the low quality of teacher professional development. Teacher professional development often goes top-down, which is one of the least effective ways. Essentially, the quality of teacher preparation should be defined by teachers. (http://www.teachology.xyz/np.htm).Large
scale changes require systemic approach. I would recommend to establish “Yidan
Institute for Advancing the World Education”. This Institute would become a
coordinating force for some of the teams nominated for the Prize, not received
it, however, expressing a certain potential (according to the criteria). The Institute
would provide some financial, logistical, organizational support (a.k.a. incubator).
Even though all the teams would work in different countries, via the
Institution they would develop, use, and when necessary modify a common protocol
for observations in education, collecting data, sharing the data, analyzing the
data.